Update README.md

README.md

@@ -9,4 +9,30 @@ tags:
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-# Hōʻike: A Joint-Embedding Predictive Architecture for Transcriptome Data Generation with Diffusion Models
+# Hōʻike: A Joint-Embedding Predictive Architecture for Transcriptome Data Generation with Diffusion Models
+
+<h3 align="right">Silico Biosciences</h3>
+
+Visit the GitHub repository for the framework code: https://github.com/silicobio/hoike
+
+
+## Usage
+
+```py
+## 1. Look up the condition samples for a tissue that exists in the normal reference set.
+user_target_tissue = "Skin"
+condition_subset = dataset.condition_df[dataset.condition_df["tissue_type"] == user_target_tissue].reset_index(drop=True)
+normal_baseline_array = dataset.normal_profiles[user_target_tissue]
+
+## 2. Generate with sampling-time normalization consistent with diffusion training.
+generated_df = generate_synthetic_condition_data_consistent(
+    normal_profile=normal_baseline_array,
+    jepa=jepa_model,
+    diffusion=diff_model,
+    scheduler=scheduler,
+    gene_cols=dataset.gene_cols,
+    num_samples=2500,
+    value_cap=condition_value_cap,
+    sampling_noise_scale=1.1,
+)
+```